{"id":"W4400228700","doi":"10.3138/jsp-2023-0019","title":"Bibliometric Methods in HRM: Contribution and Utility","year":2024,"lang":"en","type":"article","venue":"Journal of Scholarly Publishing","topic":"Business and Economic Development","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Regional science; Business; Management science; Computer science; Geography; Economics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["scholarly_communication"],"consensus_categories":["scholarly_communication"],"category_scores_codex":[0.009680531,0.00007281417,0.0001634575,0.005903394,0.00005341595,0.01135619,0.0002282048,0.00009271121,0.0005403436],"category_scores_gemma":[0.002108342,0.0000593027,0.00003876871,0.01359813,0.00003679987,0.04877916,0.0001905802,0.0006992602,0.00001524345],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003088283,"about_ca_system_score_gemma":0.00004404992,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001175804,"about_ca_topic_score_gemma":0.000015279,"domain_scores_codex":[0.9988437,0.00009324692,0.0004576758,0.0001577989,0.0002708758,0.0001767618],"domain_scores_gemma":[0.999461,0.0001599839,0.0001312163,0.00006979496,0.00005925687,0.0001187739],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.00001062406,0.00002377538,0.4019328,0.00001258302,0.00001392042,0.00003358975,0.0001602182,0.00002939856,0.001101286,0.0001836672,0.00509313,0.591405],"study_design_scores_gemma":[0.0002194869,0.00001428652,0.8632225,0.00005347103,0.000006506243,0.00008131673,0.00005238886,0.0009579727,0.0001296305,0.002778248,0.1324165,0.00006765703],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9746642,0.003619049,0.01189616,0.004359278,0.0008428215,0.00005725063,0.000001783065,0.00001180411,0.004547665],"genre_scores_gemma":[0.9763696,0.0002724484,0.02299174,0.0001899788,0.00006772718,0.000001127202,7.147961e-7,0.00000590631,0.000100813],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5913374,"threshold_uncertainty_score":0.9896701,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03621126884213235,"score_gpt":0.309543516559186,"score_spread":0.2733322477170537,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}